Premalignant Progression in the Lung: Knowledge Gaps and Novel Opportunities for Interception of Non-Small Cell Lung Cancer. An Official American Thoracic Society Research Statement.

RATIONALE: Despite significant advances in precision treatments and immunotherapy, lung cancer is the most common cause of cancer death worldwide. To reduce incidence and improve survival rates, a deeper understanding of lung premalignancy and the multistep process of tumorigenesis is essential, allowing for timely and effective intervention before cancer development. OBJECTIVES: To summarize existing information, identify knowledge gaps, formulate research questions, prioritize potential research topics, and propose strategies for future investigations into the premalignant progression in the lung. METHODS: An international multidisciplinary team of basic, translational, and clinical scientists reviewed available data to develop and refine research questions pertaining to the transformation of premalignant lung lesions to advanced lung cancer. RESULTS: This research statement identifies significant gaps in knowledge and proposes potential research questions aimed at expanding our understanding of the mechanisms underlying the progression of premalignant lung lesions to lung cancer in an effort to explore potential innovative modalities to intercept lung cancer at its nascent stages. CONCLUSIONS: The identified gaps in knowledge about the biological mechanisms of premalignant progression in the lung, along with ongoing challenges in screening, detection, and early intervention, highlight the critical need to prioritize research in this domain. Such focused investigations are essential to devise effective preventive strategies that may ultimately decrease lung cancer incidence and improve patient outcomes.

Elucidating the role of EPPK1 in lung adenocarcinoma development.

BACKGROUND: We recently found that epiplakin 1 (EPPK1) alterations were present in 12% of lung adenocarcinoma (LUAD) cases and were associated with a poor prognosis in early-stage LUAD when combined with other molecular alterations. This study aimed to identify a probable crucial role for EPPK1 in cancer development. METHODS: EPPK1 mRNA and protein expression was analyzed with clinical variables. Normal bronchial epithelial cell lines were exposed to cigarette smoke for 16 weeks to determine whether EPPK1 protein expression was altered after exposure. Further, we used CRISPR-Cas9 to knock out (KO) EPPK1 in LUAD cell lines and observed how the cancer cells were altered functionally and genetically. RESULTS: EPPK1 protein expression was associated with smoking and poor prognosis in early-stage LUAD. Moreover, a consequential mesenchymal-to-epithelial transition was observed, subsequently resulting in diminished cell proliferation and invasion after EPPK1 KO. RNA sequencing revealed that EPPK1 KO induced downregulation of 11 oncogenes, 75 anti-apoptosis, and 22 angiogenesis genes while upregulating 8 tumor suppressors and 12 anti-cell growth genes. We also observed the downregulation of MYC and upregulation of p53 expression at both protein and RNA levels following EPPK1 KO. Gene ontology enrichment analysis of molecular functions highlighted the correlation of EPPK1 with the regulation of mesenchymal cell proliferation, mesenchymal differentiation, angiogenesis, and cell growth after EPPK1 KO. CONCLUSIONS: Our data suggest that EPPK1 is linked to smoking, epithelial to mesenchymal transition, and the regulation of cancer progression, indicating its potential as a therapeutic target for LUAD.

Phases of methodological research in biostatistics-Building the evidence base for new methods.

Although new biostatistical methods are published at a very high rate, many of these developments are not trustworthy enough to be adopted by the scientific community. We propose a framework to think about how a piece of methodological work contributes to the evidence base for a method. Similar to the well-known phases of clinical research in drug development, we propose to define four phases of methodological research. These four phases cover (I) proposing a new methodological idea while providing, for example, logical reasoning or proofs, (II) providing empirical evidence, first in a narrow target setting, then (III) in an extended range of settings and for various outcomes, accompanied by appropriate application examples, and (IV) investigations that establish a method as sufficiently well-understood to know when it is preferred over others and when it is not; that is, its pitfalls. We suggest basic definitions of the four phases to provoke thought and discussion rather than devising an unambiguous classification of studies into phases. Too many methodological developments finish before phase III/IV, but we give two examples with references. Our concept rebalances the emphasis to studies in phases III and IV, that is, carefully planned method comparison studies and studies that explore the empirical properties of existing methods in a wider range of problems.

Expanding Role of Advanced Image Analysis in CT-detected Indeterminate Pulmonary Nodules and Early Lung Cancer Characterization.

The implementation of low-dose chest CT for lung screening presents a crucial opportunity to advance lung cancer care through early detection and interception. In addition, millions of pulmonary nodules are incidentally detected annually in the United States, increasing the opportunity for early lung cancer diagnosis. Yet, realization of the full potential of these opportunities is dependent on the ability to accurately analyze image data for purposes of nodule classification and early lung cancer characterization. This review presents an overview of traditional image analysis approaches in chest CT using semantic characterization as well as more recent advances in the technology and application of machine learning models using CT-derived radiomic features and deep learning architectures to characterize lung nodules and early cancers. Methodological challenges currently faced in translating these decision aids to clinical practice, as well as the technical obstacles of heterogeneous imaging parameters, optimal feature selection, choice of model, and the need for well-annotated image data sets for the purposes of training and validation, will be reviewed, with a view toward the ultimate incorporation of these potentially powerful decision aids into routine clinical practice.

Different roles of protein biomarkers predicting eGFR trajectories in people with chronic kidney disease and diabetes mellitus: a nationwide retrospective cohort study.

BACKGROUND: Chronic kidney disease (CKD) is a common comorbidity in people with diabetes mellitus, and a key risk factor for further life-threatening conditions such as cardiovascular disease. The early prediction of progression of CKD therefore is an important clinical goal, but remains difficult due to the multifaceted nature of the condition. We validated a set of established protein biomarkers for the prediction of trajectories of estimated glomerular filtration rate (eGFR) in people with moderately advanced chronic kidney disease and diabetes mellitus. Our aim was to discern which biomarkers associate with baseline eGFR or are important for the prediction of the future eGFR trajectory. METHODS: We used Bayesian linear mixed models with weakly informative and shrinkage priors for clinical predictors (n = 12) and protein biomarkers (n = 19) to model eGFR trajectories in a retrospective cohort study of people with diabetes mellitus (n = 838) from the nationwide German Chronic Kidney Disease study. We used baseline eGFR to update the models' predictions, thereby assessing the importance of the predictors and improving predictive accuracy computed using repeated cross-validation. RESULTS: The model combining clinical and protein predictors had higher predictive performance than a clinical only model, with an [Formula: see text] of 0.44 (95% credible interval 0.37-0.50) before, and 0.59 (95% credible interval 0.51-0.65) after updating by baseline eGFR, respectively. Only few predictors were sufficient to obtain comparable performance to the main model, with markers such as Tumor Necrosis Factor Receptor 1 and Receptor for Advanced Glycation Endproducts being associated with baseline eGFR, while Kidney Injury Molecule 1 and urine albumin-creatinine-ratio were predictive for future eGFR decline. CONCLUSIONS: Protein biomarkers only modestly improve predictive accuracy compared to clinical predictors alone. The different protein markers serve different roles for the prediction of longitudinal eGFR trajectories potentially reflecting their role in the disease pathway.

Randomized Trial of Etelcalcetide for Cardiac Hypertrophy in Hemodialysis.

[Figure: see text].

Sex differences in the survival benefit of kidney transplantation: a retrospective cohort study using target trial emulation.

BACKGROUND: Kidney transplantation is the preferred treatment for eligible patients with kidney failure who need renal replacement therapy. However, it remains unclear whether the anticipated survival benefit from kidney transplantation is different for women and men. METHODS: We included all dialysis patients recorded in the Austrian Dialysis and Transplant Registry who were waitlisted for their first kidney transplant between 2000 and 2018. In order to estimate the causal effect of kidney transplantation on 10-year restricted mean survival time, we mimicked a series of controlled clinical trials and applied inverse probability of treatment and censoring weighted sequential Cox models. RESULTS: This study included 4408 patients (33% female) with a mean age of 52 years. Glomerulonephritis was the most common primary renal disease both in women (27%) and men (28%). Kidney transplantation led to a gain of 2.22 years (95% CI 1.88 to 2.49) compared with dialysis over a 10-year follow-up. The effect was smaller in women (1.95 years, 95% CI 1.38 to 2.41) than in men (2.35 years, 95% CI 1.92 to 2.70) due to a better survival on dialysis. Across ages the survival benefit of transplantation over a follow-up of 10 years was smaller in younger women and men and increased with age, showing a peak for both women and men aged about 60 years. CONCLUSIONS: There were few differences in survival benefit by transplantation between females and males. Females had better survival than males on the waitlist receiving dialysis and similar survival to males after transplantation.

Evaluating methods for Lasso selective inference in biomedical research: a comparative simulation study.

BACKGROUND: Variable selection for regression models plays a key role in the analysis of biomedical data. However, inference after selection is not covered by classical statistical frequentist theory, which assumes a fixed set of covariates in the model. This leads to over-optimistic selection and replicability issues. METHODS: We compared proposals for selective inference targeting the submodel parameters of the Lasso and its extension, the adaptive Lasso: sample splitting, selective inference conditional on the Lasso selection (SI), and universally valid post-selection inference (PoSI). We studied the properties of the proposed selective confidence intervals available via R software packages using a neutral simulation study inspired by real data commonly seen in biomedical studies. Furthermore, we present an exemplary application of these methods to a publicly available dataset to discuss their practical usability. RESULTS: Frequentist properties of selective confidence intervals by the SI method were generally acceptable, but the claimed selective coverage levels were not attained in all scenarios, in particular with the adaptive Lasso. The actual coverage of the extremely conservative PoSI method exceeded the nominal levels, and this method also required the greatest computational effort. Sample splitting achieved acceptable actual selective coverage levels, but the method is inefficient and leads to less accurate point estimates. The choice of inference method had a large impact on the resulting interval estimates, thereby necessitating that the user is acutely aware of the goal of inference in order to interpret and communicate the results. CONCLUSIONS: Despite violating nominal coverage levels in some scenarios, selective inference conditional on the Lasso selection is our recommended approach for most cases. If simplicity is strongly favoured over efficiency, then sample splitting is an alternative. If only few predictors undergo variable selection (i.e. up to 5) or the avoidance of false positive claims of significance is a concern, then the conservative approach of PoSI may be useful. For the adaptive Lasso, SI should be avoided and only PoSI and sample splitting are recommended. In summary, we find selective inference useful to assess the uncertainties in the importance of individual selected predictors for future applications.

Integrated Biomarkers for the Management of Indeterminate Pulmonary Nodules.

Rationale: Patients with indeterminate pulmonary nodules (IPNs) at risk of cancer undergo high rates of invasive, costly, and morbid procedures. Objectives: To train and externally validate a risk prediction model that combined clinical, blood, and imaging biomarkers to improve the noninvasive management of IPNs. Methods: In this prospectively collected, retrospective blinded evaluation study, probability of cancer was calculated for 456 patient nodules using the Mayo Clinic model, and patients were categorized into low-, intermediate-, and high-risk groups. A combined biomarker model (CBM) including clinical variables, serum high sensitivity CYFRA 21-1 level, and a radiomic signature was trained in cohort 1 (n = 170) and validated in cohorts 2-4 (total n = 286). All patients were pooled to recalibrate the model for clinical implementation. The clinical utility of the CBM compared with current clinical care was evaluated in 2 cohorts. Measurements and Main Results: The CBM provided improved diagnostic accuracy over the Mayo Clinic model with an improvement in area under the curve of 0.124 (95% bootstrap confidence interval, 0.091-0.156; P < 2 × 10-16). Applying 10% and 70% risk thresholds resulted in a bias-corrected clinical reclassification index for cases and control subjects of 0.15 and 0.12, respectively. A clinical utility analysis of patient medical records estimated that a CBM-guided strategy would have reduced invasive procedures from 62.9% to 50.6% in the intermediate-risk benign population and shortened the median time to diagnosis of cancer from 60 to 21 days in intermediate-risk cancers. Conclusions: Integration of clinical, blood, and image biomarkers improves noninvasive diagnosis of patients with IPNs, potentially reducing the rate of unnecessary invasive procedures while shortening the time to diagnosis.

Using Background Knowledge from Preceding Studies for Building a Random Forest Prediction Model: A Plasmode Simulation Study.

There is an increasing interest in machine learning (ML) algorithms for predicting patient outcomes, as these methods are designed to automatically discover complex data patterns. For example, the random forest (RF) algorithm is designed to identify relevant predictor variables out of a large set of candidates. In addition, researchers may also use external information for variable selection to improve model interpretability and variable selection accuracy, thereby prediction quality. However, it is unclear to which extent, if at all, RF and ML methods may benefit from external information. In this paper, we examine the usefulness of external information from prior variable selection studies that used traditional statistical modeling approaches such as the Lasso, or suboptimal methods such as univariate selection. We conducted a plasmode simulation study based on subsampling a data set from a pharmacoepidemiologic study with nearly 200,000 individuals, two binary outcomes and 1152 candidate predictor (mainly sparse binary) variables. When the scope of candidate predictors was reduced based on external knowledge RF models achieved better calibration, that is, better agreement of predictions and observed outcome rates. However, prediction quality measured by cross-entropy, AUROC or the Brier score did not improve. We recommend appraising the methodological quality of studies that serve as an external information source for future prediction model development.

Risk stratification of indeterminate pulmonary nodules.

PURPOSE OF REVIEW: Lung cancer remains the leading cause of cancer-related death in the United States, with poor overall 5-year survival. Early detection and diagnosis are key to survival as demonstrated in lung cancer screening trials. However, with increasing implementation of screening guidelines and use of computed tomography, there has been a sharp rise in the incidence of indeterminate pulmonary nodules (IPNs). Risk stratification of IPNs, particularly those in the intermediate-risk category, remains challenging in clinical practice. Individual risk factors, imaging characteristics, biomarkers, and prediction models are currently used to assist in risk stratifying patients, but such strategies remain suboptimal. This review focuses on established risk stratification methods, current areas of research, and future directions. RECENT FINDINGS: The multitude of yearly incidental and screening-detected IPNs, its management-related healthcare costs, and risk of invasive procedures provides a strong rationale for risk stratification efforts. The development of new molecular and imaging biomarkers to discriminate benign from malignant lung nodules shows great promise. Yet, risk stratification methods need integration into the diagnostic workflow and await validation in prospective, biomarker-driven clinical trials. SUMMARY: Novel biomarkers and new imaging analysis, including radiomics and deep-learning methods, have been developed to optimize the risk stratification of IPNs. While promising, additional validation and clinical studies are needed before they can be part of routine clinical practice.

Unlabeled lysophosphatidic acid receptor binding in free solution as determined by a compensated interferometric reader.

Native interactions between lysophospholipids (LPs) and their cognate LP receptors are difficult to measure because of lipophilicity and/or the adhesive properties of lipids, which contribute to high levels of nonspecific binding in cell membrane preparations. Here, we report development of a free-solution assay (FSA) where label-free LPs bind to their cognate G protein-coupled receptors (GPCRs), combined with a recently reported compensated interferometric reader (CIR) to quantify native binding interactions between receptors and ligands. As a test case, the binding parameters between lysophosphatidic acid (LPA) receptor 1 (LPA1; one of six cognate LPA GPCRs) and LPA were determined. FSA-CIR detected specific binding through the simultaneous real-time comparison of bound versus unbound species by measuring the change in the solution dipole moment produced by binding-induced conformational and/or hydration changes. FSA-CIR identified KD values for chemically distinct LPA species binding to human LPA1 and required only a few nanograms of protein: 1-oleoyl (18:1; KD = 2.08 ± 1.32 nM), 1-linoleoyl (18:2; KD = 2.83 ± 1.64 nM), 1-arachidonoyl (20:4; KD = 2.59 ± 0.481 nM), and 1-palmitoyl (16:0; KD = 1.69 ± 0.1 nM) LPA. These KD values compared favorably to those obtained using the previous generation back-scattering interferometry system, a chip-based technique with low-throughput and temperature sensitivity. In conclusion, FSA-CIR offers a new increased-throughput approach to assess quantitatively label-free lipid ligand-receptor binding, including nonactivating antagonist binding, under near-native conditions.

Contribution of non-HLA incompatibility between donor and recipient to kidney allograft survival: genome-wide analysis in a prospective cohort.

BACKGROUND: The introduction of HLA matching of donors and recipients was a breakthrough in kidney transplantation. However, half of all transplanted kidneys still fail within 15 years after transplantation. Epidemiological data suggest a fundamental role of non-HLA alloimmunity. METHODS: We genotyped 477 pairs of deceased donors and first kidney transplant recipients with stable graft function at three months that were transplanted between Dec 1, 2005, and April 30, 2015. Genome-wide genetic mismatches in non-synonymous single nucleotide polymorphisms (nsSNPs) were calculated to identify incompatibilities in transmembrane and secreted proteins. We estimated the association between nsSNP mismatch and graft loss in a Cox proportional hazard model, adjusting for HLA mismatch and clinical covariates. Customised peptide arrays were generated to screen for antibodies against genotype-derived mismatched epitopes in 25 patients with biopsy-confirmed chronic antibody-mediated rejection. FINDINGS: 59 268 nsSNPs affecting a transmembrane or secreted protein were analysed. The median number of nsSNP mismatches in immune-accessible transmembrane and secreted proteins between donors and recipients was 1892 (IQR 1850-1936). The degree of nsSNP mismatch was independently associated with graft loss in a multivariable model adjusted for HLA eplet mismatch (HLA-A, HLA-B, HLA-C, HLA-DP, HLA-DQ, and HLA-DR). Each increase by a unit of one IQR had an HR of 1·68 (95% CI 1·17-2·41, p=0·005). 5-year death censored graft survival was 98% in the quartile with the lowest mismatch, 91% in the second quartile, 89% in the third quartile, and 82% in the highest quartile (p=0·003, log-rank test). Customised peptide arrays verified a donor-specific alloimmune response to genetically predicted mismatched epitopes. INTERPRETATION: Genetic mismatch of non-HLA haplotypes coding for transmembrane or secreted proteins is associated with an increased risk of functional graft loss independently of HLA incompatibility. As in HLA alloimmunity, donor-specific alloantibodies can be identified against genotype derived non-HLA epitopes. FUNDING: Austrian Science Fund, WWTF (Vienna Science and Technology Fund), and Ministry of Health of the Czech Republic.

Integrative analysis of prognostic biomarkers derived from multiomics panels helps discrimination of chronic kidney disease trajectories in people with type 2 diabetes.

Clinical risk factors explain only a fraction of the variability of estimated glomerular filtration rate (eGFR) decline in people with type 2 diabetes. Cross-omics technologies by virtue of a wide spectrum screening of plasma samples have the potential to identify biomarkers for the refinement of prognosis in addition to clinical variables. Here we utilized proteomics, metabolomics and lipidomics panel assay measurements in baseline plasma samples from the multinational PROVALID study (PROspective cohort study in patients with type 2 diabetes mellitus for VALIDation of biomarkers) of patients with incident or early chronic kidney disease (median follow-up 35 months, median baseline eGFR 84 mL/min/1.73 m2, urine albumin-to-creatinine ratio 8.1 mg/g). In an accelerated case-control study, 258 individuals with a stable eGFR course (median eGFR change 0.1 mL/min/year) were compared to 223 individuals with a rapid eGFR decline (median eGFR decline -6.75 mL/min/year) using Bayesian multivariable logistic regression models to assess the discrimination of eGFR trajectories. The analysis included 402 candidate predictors and showed two protein markers (KIM-1, NTproBNP) to be relevant predictors of the eGFR trajectory with baseline eGFR being an important clinical covariate. The inclusion of metabolomic and lipidomic platforms did not improve discrimination substantially. Predictions using all available variables were statistically indistinguishable from predictions using only KIM-1 and baseline eGFR (area under the receiver operating characteristic curve 0.63). Thus, the discrimination of eGFR trajectories in patients with incident or early diabetic kidney disease and maintained baseline eGFR was modest and the protein marker KIM-1 was the most important predictor.

Allograft and patient survival after sequential HSCT and kidney transplantation from the same donor-A multicenter analysis.

Tolerance induction through simultaneous hematopoietic stem cell and renal transplantation has shown promising results, but it is hampered by the toxicity of preconditioning therapies and graft-versus-host disease (GVHD). Moreover, renal function has never been compared to conventionally transplanted patients, thus, whether donor-specific tolerance results in improved outcomes remains unanswered. We collected follow-up data of published cases of renal transplantations after hematopoietic stem cell transplantation from the same donor and compared patient and transplant kidney survival as well as function with caliper-matched living-donor renal transplantations from the Austrian dialysis and transplant registry. Overall, 22 tolerant and 20 control patients were included (median observation period 10 years [range 11 months to 26 years]). In the tolerant group, no renal allograft loss was reported, whereas 3 were lost in the control group. Median creatinine levels were 85 µmol/l (interquartile range [IQR] 72-99) in the tolerant cohort and 118 µmol/l (IQR 99-143) in the control group. Mixed linear-model showed around 29% lower average creatinine levels throughout follow-up in the tolerant group (P < .01). Our data clearly show stable renal graft function without long-term immunosuppression for many years, suggesting permanent donor-specific tolerance. Thus sequential transplantation might be an alternative approach for future studies targeting tolerance induction in renal allograft recipients.

Quantification of Opioids in Urine Using an Aptamer-Based Free-Solution Assay.

The opioid epidemic continues in the United States. Many have been impacted by this epidemic, including neonates who exhibit Neonatal Abstinence Syndrome (NAS). Opioid diagnosis and NAS can be negatively impacted by limited testing options outside the hospital, due to poor assay performance, false-negatives, rapid drug clearance rates, and difficulty in obtaining enough specimen for testing. Here we report a small volume urine assay for oxycodone, hydrocodone, fentanyl, noroxycodone, norhydrocodone, and norfentanyl with excellent LODs and LOQs. The free-solution assay (FSA), coupled with high affinity DNA aptamer probes and a compensated interferometric reader (CIR), represents a potential solution for quantifying opioids rapidly, at high sensitivity, and noninvasively on small sample volumes. The mix-and-read test is 5- to 275-fold and 50- to 1250-fold more sensitive than LC-MS/MS and immunoassays, respectively. Using FSA, oxycodone, hydrocodone, fentanyl, and their urinary metabolites were quantified using 10 µL of urine at 28-81 pg/mL, with >95% specificity and excellent accuracy in ∼1 h. The assay sensitivity, small sample size requirement, and speed could enable opioid screening, particularly for neonates, and points to the potential for pharmacokinetic tracking.

Dialysis vintage and outcomes in renal transplantation.

Pre-emptive kidney transplantation is the recommended strategy for patients with end-stage renal failure in all guidelines [Kidney Disease: Improving Global Outcomes (KDIGO), The Australian and New Zealand Dialysis and Transplantation Registry (ANZDATA), European Renal Best Practice Guideline (ERBP), British Transplant Society (BTS)]. This recommendation is intuitive and based on few older studies with considerable limitations. In addition, there is conflicting evidence as to whether the duration of dialysis vintage impacts on graft and patient survival after transplantation. The objective of this structured review was to critically review the published evidence on dialysis vintage and outcomes by including the most recent papers on that topic. We searched Medline using keywords for kidney transplantation, pre-emptive, dialysis vintage and relevant outcomes, and found 14 eligible cohort studies. The best evidence was found for pre-emptive transplantation, which was found to be associated with a lower risk of actual graft loss (including death as event) compared with non-pre-emptive transplantation. When only patients were considered that have been registered pre-emptively but then received or did not receive a pre-emptive transplant, the association with functional graft survival (excluding death as event) was only marginal. Dialysis vintage had a graded association with patient survival in most of the studies, but an unclear estimate with functional graft survival. Older studies also found an association of dialysis vintage with death-censored graft survival, but this association is likely confounded by selection and the competing risk of death and was no longer observed in recent eras, i.e. in transplants performed in the last decade. In summary, the recommendation for pre-emptive kidney transplantation for optimal patient and graft survival remains valid even in recent periods but the association of dialysis vintage after dialysis initiation with death-censored graft survival is less clear. The association of dialysis vintage with mortality after transplantation depends on the median duration of dialysis of the wait-listed population as well as acceptance rates for transplantation, and may thus be country specific. Nevertheless, it is reasonable to advocate pre-emptive kidney transplantation in all age groups. What remains unsolved is the selection issues since the reasons for longer waiting time on dialysis are difficult to capture in retrospective observational studies, and lead time as well as immortal time bias may have confounded the mortality data.

Origin and prediction of free-solution interaction studies performed label-free.

Interaction/reaction assays have led to significant scientific discoveries in the biochemical, medical, and chemical disciplines. Several fundamental driving forces form the basis of intermolecular and intramolecular interactions in chemical and biochemical systems (London dispersion, hydrogen bonding, hydrophobic, and electrostatic), and in the past three decades the sophistication and power of techniques to interrogate these processes has developed at an unprecedented rate. In particular, label-free methods have flourished, such as NMR, mass spectrometry (MS), surface plasmon resonance (SPR), biolayer interferometry (BLI), and backscattering interferometry (BSI), which can facilitate assays without altering the participating components. The shortcoming of most refractive index (RI)-based label-free methods such as BLI and SPR is the requirement to tether one of the interaction entities to a sensor surface. This is not the case for BSI. Here, our hypothesis is that the signal origin for free-solution, label-free determinations can be attributed to conformation and hydration-induced changes in the solution RI. We propose a model for the free-solution response function (FreeSRF) and show that, when quality bound and unbound structural data are available, FreeSRF correlates well with the experiment (R(2)> 0.99, Spearman rank correlation coefficients >0.9) and the model is predictive within ∼15% of the experimental binding signal. It is also demonstrated that a simple mass-weighted dη/dC response function is the incorrect equation to determine that the change in RI is produced by binding or folding event in free solution.

Longitudinal pixel averaging for improved compensation in backscattering interferometry.

Longitudinal averaging of the interference pattern in a compensated backscattering interferometer provides improved compensation for temperature induced refractive index perturbations. Fringe pattern likeness between two discrete detection regions of an off-the-shelf microfluidic chip illuminated by an inexpensive diode laser scales with interrogation length. Averaging the intensity distribution along a 2.75 mm length of the channel results in a 750-fold reduction in sensitivity to temperature and a baseline noise level of 3×10-8 refractive index units (RIU). These observations enable nanoliter-volume interferometric measurements at a level of 10-7 RIU in the presence of a 2°C temperature variation without the need for temperature control.

Regression of left atrial diameter after kidney transplantation is associated with prolonged survival: an observational study.

Renal transplantation reduces the dramatically elevated risk of cardiovascular death in dialysis patients. We previously showed that left atrial diameter before transplantation predicts cardiovascular and overall mortality. Now, we investigated the association of changes in cardiac morphology after transplantation and mortality. We retrospectively analyzed data from the Austrian transplant repository using multivariable Cox and competing risk models and multivariable logistic regression for the prediction of changes in cardiac morphology. We identified 414 patients with a median follow-up of 8 years and observed a significant progression of mean diameter of left atrium (LA), right atrium and right ventricle and a significant regression of left ventricle. Complete case analysis of 243 patients with a regression of initially enlarged LA diameter had a significantly lower risk of adjusted overall and cardiovascular mortality; hazard ratio (HR 0.45, 95% CI 0.30-0.69, P < 0.001, 124 deaths), and HR of 0.43 [95% CI 0.21-0.92, P = 0.029, 48 cardiovascular (CV) deaths], respectively. Only age at transplantation was significantly associated with regression of LA (OR 0.75, 95% CI 0.60-0.93, P = 0.007). Patients with regression of LA after kidney transplantation exhibited a lower overall and CV mortality risk. Besides age, peritoneal dialysis and antihypertensive therapy were mediators of LA regression.